| NSF Org: |
OISE Office of International Science and Engineering |
| Recipient: |
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| Initial Amendment Date: | September 12, 2019 |
| Latest Amendment Date: | September 12, 2019 |
| Award Number: | 1940619 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Maija Kukla
mkukla@nsf.gov (703)292-4940 OISE Office of International Science and Engineering O/D Office Of The Director |
| Start Date: | February 1, 2020 |
| End Date: | January 31, 2022 (Estimated) |
| Total Intended Award Amount: | $74,997.00 |
| Total Awarded Amount to Date: | $74,997.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
104 E UNIVERSITY AVE LAFAYETTE LA US 70503-2014 (337)482-5811 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
301 E Lewis, POBox 41771 Lafayette LA US 70504-1210 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | IRES Track I: IRES Sites (IS) |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.079 |
ABSTRACT
Video streaming is the major source of Internet traffic in the U.S. and worldwide. To enable high quality and fast video streaming, independent from the viewers' geographical location, researchers propose to explore a inter-fog computing system called Latency Aware STreaming (LAST) that enables multi-source video streaming. Leveraging the locality feature exists in video stream requests, LAST will be able to partially pre-process video streams and stream the missing segments simultaneously from peer LASTs that possibly have those missing segments cached. LASTs also have the ability to locally process video streams. Therefore, for each video segment, a LAST has to determine whether to stream it from a neighboring LAST or process it locally. To reduce streaming latency further, the LAST platform proactively determines hot videos in a certain jurisdiction and preheats those videos at the fog level.
This is an exploratory but potentially transformative research idea in its early stages. This project, involving the radical approach of multi-source video streaming, is considered high risk/ potentially high payoff. The project is interdisciplinary and combines cloud/fog computing and video streaming. To explore this idea, two students (possibly one graduate and one
undergraduate student) are expected to travel to visit CLOUDS Lab at Melbourne University in Australia that is directed by Dr. Rajkumar Buyya, one of the most outstanding researchers in cloud computing and he is the highest cited scientist in Cloud Computing. In a close collaboration with CLOUDS lab, the team of researchers will learn working with iFogSim, which is a platform for
simulating and testing fog computing systems developed at CLOUDS Lab. It will enable students to simulate and prototype the proposed
LAST platform. During the visit, students will learn and extend iFogSim to accommodate video streaming tasks and achieve multi-source
streaming via inter-fog systems. This project will help undergraduate and graduate students from UL
Lafayette to become highly skilled and globally engaged workforce.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The project provided the opportunity for four U.S. participants, including two graduate and two undergraduate students, to visit Clouds Lab at Melbourne University and earn both the research and cultural experiences. Due to the COVID-19 restrictions, this research visit had to be conducted virtually. The project participants were carefully selected based on their merits and interests into the research topic. The participants were trained about the basics of cloud and distributed computing during the Spring semester 2021 and got prepared to conduct the research projects. In the next phase, each participant was assigned a research problem to work on during the eight-week research visit. The participants conducted research to enable low-latency task processing via efficient resource allocation in an inter-fog computing system. As a result of this project, the following artifacts were produced: (A) Novel methods developed for QoS- and Energy-aware multi-source video streaming and for load-balancing in the inter-fog environment; (B) The idea of distributed machine learning inference (also known as, inference partitioning) was explored in this context by making use of the MLPerf and Tensorflow Serving platforms; and (C) A monitoring tool for task scheduling in the fog/cloud computing environment was developed. This project impacted the educational, research, and cultural experiences of its participants. In addition, the findings of this project can potentially help in developing solutions for more efficient and flexible video streaming services in the fog- and cloud-based environments.
Last Modified: 01/09/2022
Modified by: Mohsen Amini Salehi
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